The present invention relates generally to the electrical, electronic and computer arts, and, more particularly, to geographical information management.
The ubiquitous availability of location sensing devices embedded in smartphones, vehicles, and other devices, combined with the ability to collect data at scale, enables fine-grained monitoring and modeling of human movement, both at an individual level and at a group level. Using trajectory data harvested by global positioning systems (GPS), radio frequency identification (RFID) and mobile devices, complex pattern queries can be posed against objects moving in both time and space. Answering these queries introduces opportunities for business intelligence, where prediction regarding future patterns can be used to solve challenging problems such as, but not limited to, traffic congestion prediction, crime pattern analysis and prediction, epidemic spread characterization and alerting, insurance pricing, and targeted advertising.
One significant challenge posed by processing spatial queries is the sheer amount of available spatial data; the volume of such data is increasing at an unprecedented rate, primarily attributable to the widespread use of GPS-enabled smart-phones and the like. In this context, high performance techniques are needed to process spatial queries in a reasonable amount of time.
Geohash is a hierarchical geocoding system that is often used for spatial indexing. Geohash presents several advantages over a traditional latitude/longitude geographic coordinate system, such as, for example, efficient indexing, support for hierarchical regions, arbitrary precision, and simple proximity estimation. However, geohashes do not replace latitude/longitude coordinates, primarily due to the format of disseminated data, as well as the dependency of several spatial algorithms on latitude/longitude coordinates. Accordingly, geohash codes often require conversion to and from other geographical data formats.